Machine learning and applications in ultrafast photonics


Recent years have seen the rapid growth and development of the field of smart photonics, where machine-learning algorithms are being matched to optical systems to add new functionalities and to enhance performance. An area where machine learning shows particular potential to accelerate technology is the field of ultrafast photonics — the generation and characterization of light pulses, the study of light–matter interactions on short timescales, and high-speed optical measurements. Our aim here is to highlight a number of specific areas where the promise of machine learning in ultrafast photonics has already been realized, including the design and operation of pulsed lasers, and the characterization and control of ultrafast propagation dynamics. We also consider challenges and future areas of research.

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Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Additional Information: Copyright © 2020, Springer Nature Limited Funding Information: G.G. acknowledges the Academy of Finland (318082, 333949, Flagship PREIN 320165). L.S. acknowledges the Faculty of Engineering and Natural Sciences graduate school of Tampere University. J.M.D. and D.B. were supported by the EUR EIPHI and I-SITE BFC projects (contracts ANR-17-EURE-0002 and ANR-15-IDEX-0003). D.B. also acknowledges funding from the Volkswagen Foundation and from the French Agence Nationale de la Recherche (ANR-19-CE24-0006-02). The work of S.K.T. and A.K. was supported by the Russian Science Foundation (grant number 17-72-30006). S.K.T. acknowledges the support of the EPSRC project TRANSNET. The work of S.K. was supported by the Russian Foundation for Basic Research grant number 18-29-20025.
Uncontrolled Keywords: Electronic, Optical and Magnetic Materials,Atomic and Molecular Physics, and Optics
Publication ISSN: 1749-4885
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.nat ... 566-020-00716-4 (Publisher URL)
PURE Output Type: Review article
Published Date: 2021-02
Published Online Date: 2020-11-30
Accepted Date: 2020-10-08
Authors: Genty, Goëry
Salmela, Lauri
Dudley, John M.
Brunner, Daniel
Kokhanovskiy, Alexey
Kobtsev, Sergei
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)



Version: Accepted Version

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